Short Communication: Forage mixture responses to water stress in semi-arid prairie grassland: a pot experiment
Bibliographic record
Abstract
Wang, Z., Schellenberg, M. P., Biligetu, B., Zhao, M. L. and Han, G. D. 2012. Short Communication: Forage mixture responses to water stress in semi-arid prairie grassland: a pot experiment. Can. J. Plant Sci. 92: 1259–1261. As a negative result of changing climate, drought has become a worldwide concern, particularly in arid and semiarid regions. Under drier conditions, seeding multiple forage species may produce higher biomass than single species. A randomized complete block design experiment was carried out in a growth chamber over a 4-mo period to examine the effect of watering regimes (100, 85 and 70% of field capacity of semiarid grassland) on above- and below-ground biomass of various combinations of five forage species. Alfalfa monoculture and mixtures containing alfalfa produced significantly higher above- and below-ground biomass (P<0.05) than the other species or species combinations when grown at field capacity. They were also among the highest biomass producers under restricted water supply, although differences were not always statistically different. Winterfat showed a good tolerance to water deficit and its below-ground biomass production was not significantly affected by water restriction. The findings suggest that a mixture of native plant species with alfalfa would be important for forage seedling production in the semi-arid prairie grassland under water-limiting conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".